Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids

• URL: http://arxiv.org/abs/2502.20396v1
• Date: Thu, 27 Feb 2025 18:59:52 GMT
• Title: Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids
• Authors: Toru Lin, Kartik Sachdev, Linxi Fan, Jitendra Malik, Yuke Zhu,
• Abstract summary: This work investigates the key challenges in applying reinforcement learning to solve a collection of contact-rich manipulation tasks on a humanoid embodiment.<n>Our main contributions include an automated real-to-sim tuning module that brings the simulated environment closer to the real world.<n>We show promising results on three humanoid dexterous manipulation tasks, with ablation studies on each technique.
• Score: 61.033745979145536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement learning has delivered promising results in achieving human- or even superhuman-level capabilities across diverse problem domains, but success in dexterous robot manipulation remains limited. This work investigates the key challenges in applying reinforcement learning to solve a collection of contact-rich manipulation tasks on a humanoid embodiment. We introduce novel techniques to overcome the identified challenges with empirical validation. Our main contributions include an automated real-to-sim tuning module that brings the simulated environment closer to the real world, a generalized reward design scheme that simplifies reward engineering for long-horizon contact-rich manipulation tasks, a divide-and-conquer distillation process that improves the sample efficiency of hard-exploration problems while maintaining sim-to-real performance, and a mixture of sparse and dense object representations to bridge the sim-to-real perception gap. We show promising results on three humanoid dexterous manipulation tasks, with ablation studies on each technique. Our work presents a successful approach to learning humanoid dexterous manipulation using sim-to-real reinforcement learning, achieving robust generalization and high performance without the need for human demonstration.

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